在大型并行电池组中使用电流分布检测有缺陷的离子电池
Pierre Lambert1,2, Ross Drummond3, Joseph P Ross1,4
1Department of Engineering Science, University of Oxford, Oxford, UK.
Communications engineering
|January 21, 2026
概括
使用支持矢量机器的新型机器学习分类器可以以83%的准确度检测大型电池组中的电池故障. 这种电池管理系统的进步通过使用有限电流传感器来提高安全性.
科学领域:
- 电池技术是电池的技术.
- 机器学习应用程序 机器学习应用程序
- 电化学系统 电化学系统
背景情况:
- 电池组安全是阻碍采用电池组的一个主要问题,主要是由于电池故障造成的火灾风险.
- 了解大包动态和改进电池管理系统 (BMS) 对于减轻这些风险至关重要.
研究的目的:
- 开发一种机器学习分类器,用于检测大型电池组中的电池故障.
- 用有限数量的电流传感器来有效检测故障.
主要方法:
- 基于支持矢量机器 (SVM) 的机器学习分类器被开发出来.
- 为并行连接的电池组引入了一个建模框架,并与Doyle-Fuller-Newman电化学模型进行了验证.
- 将分类器训练并使用当前传感器数据进行测试.
主要成果:
- 开发的分类器在检测细胞故障方面取得了83%的令人满意的准确性.
- 有效的故障分类是使用当前信息从只有27%的细胞完成的.
- 该模型的性能通过使用实验包数据进一步验证.
结论:
- 结合数学建模和机器学习,为增强电池管理系统提供了一个有前途的方法.
- 这种方法可以有效地解决与大型电池组相关的复杂性,并提高整体安全性.
- 该研究表明,在减少传感器部署的情况下,精确检测细胞故障的潜力很大.
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